





'''simple docstring'''






from PIL import Image






def   __A    (				a_    :	Image   ,a_    :	int						):
		lowerCAmelCase  :     Dict       						= (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))

		def contrast(a_    :	int						) -> int:
				return int(1_2_8 + factor * (c - 1_2_8)						)

		return img.point(a_						)


if __name__ == "__main__":
					# Load image
					with Image.open("""image_data/lena.jpg""") as img:
										# Change contrast to 170
										lowerCAmelCase									=   change_contrast(img, 1_70)
										cont_img.save("""image_data/lena_high_contrast.png""", format="""png""")










'''simple docstring'''






import math

import tensorflow as tf
from packaging import version






def   __A    (				a_    :	List[Any]						):
		lowerCAmelCase  :     Any       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     List[Any]       						= 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)   ,x.dtype						)						))

		return x * cdf






def   __A    (				a_    :	int						):
		lowerCAmelCase  :     Dict       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     int       						= tf.cast(math.pi   ,x.dtype						)
		lowerCAmelCase  :     Dict       						= tf.cast(0.0_4_4_7_1_5   ,x.dtype						)
		lowerCAmelCase  :     Dict       						= 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(a_   ,3						))						))

		return x * cdf






def   __A    (				a_    :	Union[str, Any]						):
		lowerCAmelCase  :     Any       						= tf.convert_to_tensor(a_						)

		return x * tf.tanh(tf.math.softplus(a_						)						)






def   __A    (				a_    :	List[str]						):
		lowerCAmelCase  :     Dict       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     Any       						= tf.cast(0.0_4_4_7_1_5   ,x.dtype						)
		lowerCAmelCase  :     Optional[int]       						= tf.cast(0.7_9_7_8_8_4_5_6_0_8   ,x.dtype						)

		return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))






def   __A    (				a_    :	Union[str, Any]						):
		lowerCAmelCase  :     Optional[int]       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     List[Any]       						= tf.cast(1.7_0_2   ,x.dtype						)
		return x * tf.math.sigmoid(coeff * x						)






def   __A    (				a_    :	List[Any]						):
		return tf.clip_by_value(_gelu(a_						)   ,-1_0   ,1_0						)






def   __A    (				a_    :	List[Any]   ,a_    :	List[Any]=-1						):
		lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= tf.split(a_   ,2   ,axis=a_						)
		return a * tf.math.sigmoid(a_						)


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):






					def   __A    (				a_    :	Optional[Any]						):
							return tf.keras.activations.gelu(a_   ,approximate=a_						)

					lowerCAmelCase									=   tf.keras.activations.gelu

					lowerCAmelCase									=   approximate_gelu_wrap
else:
					lowerCAmelCase									=   _gelu
					lowerCAmelCase									=   _gelu_new


lowerCAmelCase									=   {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}






def   __A    (				a_    :	int						):
		if activation_string in ACTaFN:
				return ACTaFN[activation_string]
		else:
				raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}'''						)



